{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    " \n",
    "import scipy.misc\n",
    "import time\n",
    "import os\n",
    "import h5py\n",
    "from scipy.ndimage.filters import gaussian_filter, median_filter\n",
    "\n",
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Conv2D, Input, MaxPooling2D, Flatten, Dense, Dropout\n",
    "from keras import backend as K\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten (Flatten)            (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "fc1 (Dense)                  (None, 4096)              102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Dense)                  (None, 4096)              16781312  \n",
      "_________________________________________________________________\n",
      "predictions (Dense)          (None, 1000)              4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# path to the model weights file.\n",
    "weights_path = 'weight/vgg16_weights_tf_dim_ordering_tf_kernels.h5'\n",
    " \n",
    "def VGG_16(w_path=None):\n",
    "    \n",
    "    img_input = Input(shape=(224,224,3))\n",
    "    \n",
    "    # Block 1\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "\n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "\n",
    "        # Classification block\n",
    "    x = Flatten(name='flatten')(x)\n",
    "    x = Dense(4096, activation='relu', name='fc1')(x)\n",
    "    # x = Dropout(0.5)(x)\n",
    "    x = Dense(4096, activation='relu', name='fc2')(x)\n",
    "    # x = Dropout(0.5)(x)\n",
    "    x = Dense(1000, activation='linear', name='predictions')(x) # avoid softmax (see Simonyan 2013)\n",
    "\n",
    "    model = Model(img_input, x, name='vgg16')\n",
    "    \n",
    "    if w_path:\n",
    "        model.load_weights(w_path)\n",
    " \n",
    "    return model\n",
    " \n",
    "# Creates the VGG models and loads weights\n",
    "vgg16 = VGG_16()\n",
    "print('Model loaded.')\n",
    "vgg16.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25088, 4096)\n",
      "Successfully transformed!\n",
      "Model loaded.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "fc1 (Conv2D)                 (None, 7, 7, 4096)        102764544 \n",
      "_________________________________________________________________\n",
      "fc2 (Conv2D)                 (None, 7, 7, 4096)        16781312  \n",
      "_________________________________________________________________\n",
      "predictions_1000 (Conv2D)    (None, 7, 7, 1000)        4097000   \n",
      "=================================================================\n",
      "Total params: 138,357,544\n",
      "Trainable params: 138,357,544\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def transfer_FCN_Vgg16():\n",
    "    input_shape = (224,224,3)\n",
    "    img_input = Input(shape=input_shape)\n",
    "    # Block 1\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "\n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "\n",
    "    # Convolutional layers transfered from fully-connected layers\n",
    "    x = Conv2D(4096, (7, 7), activation='relu', padding='same', name='fc1')(x)\n",
    "    x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2')(x)\n",
    "    x = Conv2D(1000, (1, 1), activation='linear', name='predictions_1000')(x)\n",
    "    #x = Reshape((7,7))(x)\n",
    "\n",
    "    # Create model\n",
    "    model = Model(img_input, x)\n",
    "    weights_path = \"weight/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5\"\n",
    "\n",
    "    #transfer if weights have not been created\n",
    "    if os.path.isfile(weights_path) == False:\n",
    "        flattened_layers = model.layers\n",
    "        index = {}\n",
    "        for layer in flattened_layers:\n",
    "            if layer.name:\n",
    "                index[layer.name]=layer\n",
    "\n",
    "        for layer in vgg16.layers:\n",
    "            weights = layer.get_weights()\n",
    "            if layer.name=='fc1':\n",
    "                print(weights[0].shape)\n",
    "                # weights[0] = np.reshape(weights[0], (7,7,512,4096))\n",
    "                weights[0] = np.reshape(weights[0], (7,7,512,4096))\n",
    "            elif layer.name=='fc2':\n",
    "                weights[0] = np.reshape(weights[0], (1,1,4096,4096))\n",
    "            elif layer.name=='predictions':\n",
    "                layer.name='predictions_1000'\n",
    "                weights[0] = np.reshape(weights[0], (1,1,4096,1000))\n",
    "            if layer.name in index:\n",
    "                index[layer.name].set_weights(weights)\n",
    "        model.save_weights(weights_path)\n",
    "        print( 'Successfully transformed!')\n",
    "    #else load weights\n",
    "    else:\n",
    "        model.load_weights(weights_path, by_name=True)\n",
    "        print( 'Already transformed!')\n",
    "        \n",
    "    return model\n",
    "\n",
    "fcn_vgg16 = transfer_FCN_Vgg16()\n",
    "print('Model loaded.')\n",
    "fcn_vgg16.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processing filter 1\n",
      "\t0, Current loss value:-0.264918\n",
      "\t50, Current loss value:505.628479\n",
      "\t100, Current loss value:625.367493\n",
      "\t150, Current loss value:819.364929\n",
      "\t200, Current loss value:821.240417\n",
      "\t250, Current loss value:794.153992\n",
      "\t300, Current loss value:918.415283\n",
      "\t350, Current loss value:907.440613\n",
      "\t400, Current loss value:844.815430\n",
      "\t450, Current loss value:959.737793\n",
      "\t500, Current loss value:934.711304\n",
      "\t550, Current loss value:874.333862\n",
      "\t600, Current loss value:986.298584\n",
      "\t650, Current loss value:955.244873\n",
      "\t700, Current loss value:890.944153\n",
      "\t750, Current loss value:1002.138550\n",
      "\t800, Current loss value:958.879211\n",
      "\t850, Current loss value:896.758484\n",
      "\t900, Current loss value:1010.898499\n"
     ]
    }
   ],
   "source": [
    "img_width = 224\n",
    "img_height = 224\n",
    "\n",
    "# the name of the layer we want to visualize\n",
    "# (see model definition at keras/applications/vgg16.py)\n",
    "layer_name = 'predictions_1000'\n",
    "\n",
    "# util function to convert a tensor into a valid image\n",
    "\n",
    "\n",
    "def deprocess_image(x):\n",
    "    # normalize tensor: center on 0., ensure std is 0.1\n",
    "    x -= x.mean()\n",
    "    x /= (x.std() + 1e-5)\n",
    "    x *= 0.1\n",
    "\n",
    "    # clip to [0, 1]\n",
    "    x += 0.5\n",
    "    x = np.clip(x, 0, 1)\n",
    "\n",
    "    # convert to RGB array\n",
    "    x *= 255\n",
    "    if K.image_data_format() == 'channels_first':\n",
    "        x = x.transpose((1, 2, 0))\n",
    "    x = np.clip(x, 0, 255).astype('uint8')\n",
    "    return x\n",
    "\n",
    "# this is the placeholder for the input images\n",
    "input_img = fcn_vgg16.input\n",
    "\n",
    "# get the symbolic outputs of each \"key\" layer (we gave them unique names).\n",
    "layer_dict = dict([(layer.name, layer) for layer in fcn_vgg16.layers[1:]])\n",
    "\n",
    "\n",
    "def normalize(x):\n",
    "    # utility function to normalize a tensor by its L2 norm\n",
    "    return x / (K.sqrt(K.mean(K.square(x))) + 1e-5)\n",
    "\n",
    "\n",
    "\"\"\"\n",
    "imagenet1000_clsid_to_human.txt from https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a\n",
    "{\n",
    "    1: 'goldfish, Carassius aurtus',\n",
    "    8: 'hen',\n",
    "    18: 'magpie',\n",
    "    388: 'giant panda, panda, panda bear, coon bear, Ailuropoda melanoleuca',\n",
    "    351: 'hartebeest',\n",
    "    327: 'starfish, sea star',\n",
    "    413: 'assault rifle, assault gun',\n",
    "    672: 'mountain tent',\n",
    "}\n",
    "\"\"\"\n",
    "kept_filters = []\n",
    "all_filter = [1, 8, 18, 388, 351, 327, 413, 672]\n",
    "\n",
    "for ix, filter_index in enumerate([1]):\n",
    "    # we only scan through the first 200 filters,\n",
    "    # but there are actually 512 of them\n",
    "    print('Processing filter %d' % filter_index)\n",
    "    start_time = time.time()\n",
    "\n",
    "    # we build a loss function that maximizes the activation\n",
    "    # of the nth filter of the layer considered\n",
    "    layer_output = layer_dict[layer_name].output\n",
    "    loss = K.mean(layer_output[:, filter_index, :, :])\n",
    "\n",
    "    # we compute the gradient of the input picture wrt this loss\n",
    "    grads = K.gradients(loss, input_img)[0]\n",
    "\n",
    "    # normalization trick: we normalize the gradient\n",
    "    grads = normalize(grads)\n",
    "\n",
    "    # this function returns the loss and grads given the input picture\n",
    "    iterate = K.function([input_img], [loss, grads])\n",
    "\n",
    "    # step size for gradient ascent\n",
    "    step = 6.\n",
    "\n",
    "    # we start from a gray image with some random noise\n",
    "    input_img_data = np.random.random((1, 3, img_width, img_height))\n",
    "    input_img_data = (input_img_data - 0.5) * 20 + 128\n",
    "\n",
    "    mFilterSize = 3\n",
    "    mFilterEvery = 30\n",
    "    mIter = 1001\n",
    "    # step 5, mFilterEvery 8, mFilterSize 3\n",
    "\n",
    "    # we run gradient ascent for 20 steps\n",
    "    for i in range(mIter):\n",
    "        loss_value, grads_value = iterate([input_img_data])\n",
    "        input_img_data += grads_value * step\n",
    "\n",
    "        input_img_data = np.clip(input_img_data, 0., 255.)\n",
    "                        \n",
    "        if mFilterSize is not 0 and i % mFilterEvery == 0 :\n",
    "            input_img_data = median_filter(input_img_data, size=(1, 1, mFilterSize, mFilterSize))\n",
    "    \n",
    "        if i % 50 == 0:\n",
    "            print('\\t%d, Current loss value:%f' % (i, loss_value))\n",
    "\n",
    "    # decode the resulting input image\n",
    "    img = deprocess_image(input_img_data[0])\n",
    "    kept_filters.append((img, loss_value, filter_index))\n",
    "    end_time = time.time()\n",
    "    print('%d, Filter %d processed in %ds' % (ix, filter_index, end_time - start_time))\n",
    "    \n",
    "print(\"=\"*80)\n",
    "print(\"Finish!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(224, 224, 3)\n",
      "1\n"
     ]
    },
    {
     "data": {
      "image/png": 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WkSiJgFC1clFhrRM3+dqEoQgRQ8nBbz0RUFOChCNGmUMgp8j9dI8nPGRUg2s9mt+LK+OO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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5a5c0e0908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(kept_filters[0][0])\n",
    "print(kept_filters[0][0].shape)\n",
    "print(len(kept_filters))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for img_idx in kept_filters:\n",
    "    img = img_idx[0]\n",
    "    scipy.misc.imsave('%s_lr5_it1000_mf510.jpg' % (img_idx[2]), img)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model loaded.\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_3 (InputLayer)         (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "fc1 (Conv2D)                 (None, 7, 7, 4096)        52432896  \n",
      "_________________________________________________________________\n",
      "fc2 (Conv2D)                 (None, 7, 7, 4096)        16781312  \n",
      "_________________________________________________________________\n",
      "predictions_1000 (Conv2D)    (None, 7, 7, 1000)        4097000   \n",
      "=================================================================\n",
      "Total params: 88,025,896\n",
      "Trainable params: 88,025,896\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "def transfer_FCN_Vgg16():\n",
    "    input_shape = (224,224,3)\n",
    "    img_input = Input(shape=input_shape)\n",
    "    # Block 1\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)\n",
    "    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block1_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)\n",
    "\n",
    "    # Block 2\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)\n",
    "    x = Conv2D(128, (3, 3), activation='relu', padding='same', name='block2_conv2')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)\n",
    "\n",
    "    # Block 3\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv2')(x)\n",
    "    x = Conv2D(256, (3, 3), activation='relu', padding='same', name='block3_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)\n",
    "\n",
    "    # Block 4\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block4_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)\n",
    "\n",
    "    # Block 5\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv1')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv2')(x)\n",
    "    x = Conv2D(512, (3, 3), activation='relu', padding='same', name='block5_conv3')(x)\n",
    "    x = MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)\n",
    "\n",
    "    # Convolutional layers transfered from fully-connected layers\n",
    "    x = Conv2D(4096, (5, 5), activation='relu', padding='same', name='fc1')(x)\n",
    "    x = Conv2D(4096, (1, 1), activation='relu', padding='same', name='fc2')(x)\n",
    "    x = Conv2D(1000, (1, 1), activation='linear', name='predictions_1000')(x)\n",
    "    #x = Reshape((7,7))(x)\n",
    "\n",
    "    # Create model\n",
    "    model = Model(img_input, x)\n",
    "    weights_path = \"weight/fcn_vgg16_weights_tf_dim_ordering_tf_kernels.h5\"\n",
    "\n",
    "    #transfer if weights have not been created\n",
    "#     if os.path.isfile(weights_path) == False:\n",
    "#         flattened_layers = model.layers\n",
    "#         index = {}\n",
    "#         for layer in flattened_layers:\n",
    "#             if layer.name:\n",
    "#                 index[layer.name]=layer\n",
    "\n",
    "#         for layer in vgg16.layers:\n",
    "#             weights = layer.get_weights()\n",
    "#             if layer.name=='fc1':\n",
    "#                 print(weights[0].shape)\n",
    "#                 # weights[0] = np.reshape(weights[0], (7,7,512,4096))\n",
    "#                 weights[0] = np.reshape(weights[0], (7,7,512,4096))\n",
    "#             elif layer.name=='fc2':\n",
    "#                 weights[0] = np.reshape(weights[0], (1,1,4096,4096))\n",
    "#             elif layer.name=='predictions':\n",
    "#                 layer.name='predictions_1000'\n",
    "#                 weights[0] = np.reshape(weights[0], (1,1,4096,1000))\n",
    "#             if layer.name in index:\n",
    "#                 index[layer.name].set_weights(weights)\n",
    "#         model.save_weights(weights_path)\n",
    "#         print( 'Successfully transformed!')\n",
    "#     #else load weights\n",
    "#     else:\n",
    "#         model.load_weights(weights_path, by_name=True)\n",
    "#         print( 'Already transformed!')\n",
    "        \n",
    "    return model\n",
    "\n",
    "fcn_vgg16 = transfer_FCN_Vgg16()\n",
    "print('Model loaded.')\n",
    "fcn_vgg16.summary()"
   ]
  },
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   "execution_count": null,
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   },
   "outputs": [],
   "source": []
  }
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